BETaaS: A Platform for Development and Execution of Machine-to-Machine Applications in the Internet of Things

The integration of everyday objects into the Internet represents the foundation of the forthcoming Internet of Things (IoT). Such “smart” objects will be the building blocks of the next generation of applications that will exploit interaction between machines to implement enhanced services with minimum or no human intervention in the loop. A crucial factor to enable Machine-to-Machine (M2M) applications is a horizontal service infrastructure that seamlessly integrates existing IoT heterogeneous systems. The authors present BETaaS, a framework that enables horizontal M2M deployments. BETaaS is based on a distributed service infrastructure built on top of an overlay network of gateways that allows seamless integration of existing IoT systems. The platform enables easy deployment of applications by exposing to developers a service oriented interface to access things (the Things-as-a-Service model) regardless of the technology and the physical infrastructure they belong

An Improved Boosting Algorithm and its Application to Text Categorization

Abstract Boosting is a classification method which has successfully been applied to many different domains, and that has proven one of the best performers in text categorization (TC) exercises so far. Boosting is based on the idea of relying on the collective judgment of a committee of classifiers that are trained sequentially. In training the i-th classifier special emphasis is placed on the correct classification of the training documents which have proven harder for the previously trained classifiers. In this paper we describe an improved boosting algorithm, called AdaBoost.MH RK, and its application to text categorization. AdaBoost.MH RK is based on the idea to build, at every iteration of the learning phase, not a single classifier but a sub-committee of the k classifiers that, at this iteration, look the most promising. We report the results of systematic experimentation of this method performed on the standard Reuters-21578 benchmark. These experiments have shown that AdaBoost.MH RK is both more efficient to train and more effective than th

An improved boosting algorithm and its application to text categorization

Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7 Rome / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Opposition in transition: pre-electoral coalitions and the 2018 electoral breakthrough in Malaysia

In May 2018, the Malaysian opposition coalition Pakatan Harapan or Hope Alliance won the federal elections for the first time in the history of the country. The electoral authoritarian system is now in a state of transition. The electoral breakthrough was the result of longer-term socio-economic transformations, but the formation of a strong pre-electoral coalition was ultimately decisive for the victory. The article compares various coalitions and their performance during seven elections since 1990. The structured, focused comparison analyses the coalitions during this period because prior to 1990 the opposition was fragmented. On the basis of a three-level concept regarding the strength of pre-electoral coalitions, the article argues that Pakatan Harapan was successful because the coalition was sufficiently comprehensive (as indicated by the number and the competitiveness of candidates), cohesive (concerning ideological proximity and behavioural routinization) and well-rooted in society (in terms of linkages to voters/supporters and to civil society networks or organizations)